Overview

This tutorial describes the data reduction of the HI spectral line observed of the nearby (4.8 Mpc), gas-rich dwarf galaxy LEDA44055 (Figure 1: grab HST image). For its gas-rich nature, LEDA44055 is quiescent and is an Hα non-detection. Observations by the HST show a weak blue plume structure, and further inspection of GALEX archival images show some faint emission in the optical body, which taken together suggest that LEDA44055 may be in a "post-starburst" phase.

In this 1.4 GHz observation, a 16 MHz wide subband (spectral window, abbreviated as spw in CASA) using 4096 channels was observed, each channel providing ~ 1700 km/s of velocity coverage that results in a channel width of 3.906 kHz per channel. The second IF subband is used to acquire 1-2 GHz continuum imaging of the field; if LEDA44055 is in post-starbust phase, then it may display significant synchotron emission.

The TDEM0025 observation at the VLA was done during C-configuration and spanned 2 hours on the instrument from 31 July 2017 at 12:39 UT to 1 August 2017 at 14:39 UT. Information about the observation can be found on the corresponding VLA Observing Log and the continuation log (the observing log was split at the monthly boundary). This observing log is a record of events that transpired during the observation of the project, including weather conditions and any loss of antenna(s) and/or components that could affect the outcome of the observation.

This observation of LEDA44055 was taken as part of the Observing for University Classes program. The project code for this VLA observation is TDEM0025. This paper by Cannon et. al is the result of the observation.

How to use this CASA guide

Please use CASA 5.4.0 for this tutorial (typing casa -ls in a linux window shows the available versions and the current version; to explicitly change the current version type, e.g., casa -r 5.4.0-68)

There are at least three different ways to interact with CASA, described in more detail in Getting Started in CASA. In this guide we provide the pseudo-interactive method for every step and the interactive method for some of the steps. Since it is possible to use both methods, take care not to run the same task with identical parameters twice using both methods.

Interactively examining task inputs. In this mode, one types taskname to load the task, inp to examine the inputs (see Figure 2), and go once those inputs have been set to your satisfaction. Allowed inputs are shown in blue and bad inputs are colored red. The input parameters themselves are changed one by one, e.g., selectdata=True. Summaries of the inputs to various tasks used in the data reduction below are provided, to illustrate which parameters need to be set. More detailed help can be obtained on any task by typing help taskname. Once a task is run, the set of inputs are stored and can be retrieved via tget taskname; subsequent runs will overwrite the previous tget file. To reset a task to its default settings type, default taskname.

Pseudo-interactively via task function calls. In this case, all of the desired inputs to a task are provided at once on the CASA command line. This tutorial is made up of such calls, which were developed by looking at the inputs for each task and deciding what needed to be changed from default values. For task function calls, only parameters that you want to be different from their defaults need to be set.

# Pseudo-interactive CASAtaskname('input parameters')

Non-interactively via a script. A series of task function calls can be combined together into a script, and run from within CASA via execfile('scriptname.py'). This and other CASA Tutorial Guides have been designed to be extracted into a script via the script extractor by using the method described within the Extracting scripts from these tutorials page. Should you use the script generated by the script extractor for this CASA Guide, be aware that it will require some small amount of interaction related to the plotting, occasionally suggesting that you close the graphics window and hitting return in the terminal to proceed. It is in fact unnecessary to close the graphics windows (it is suggested that you do so purely to keep your desktop uncluttered).

Step 1: Obtain the Dataset

From the NRAO Data Archive enter TDEM0025 in the Project Code field and select the data set TDEM0025.sb34039638.eb34043648.57965.965492013886. (This should be the only dataset for the project). The dataset on the archive is around 85 GB in size.

You will download the dataset as an SDM file, either as a .tar file or as an uncompressed file. Under the Jansky VLA datasets options, check the "SDM-BDF dataset (all files)" button and, if you want the dataset downloaded as a .tar file, check the "Create tar file" box.

Once you have your dataset, copy it into a directory where you can launch CASA to begin the data reduction steps below. If you downloaded the dataset as a .tar file, you need to perform the following extra step to extract the dataset before beginning the data reduction steps.

Step 2: Import the Dataset into CASA

In earlier versions of CASA, you would import your data using the CASA command importevla. With CASA 5.4.0 and higher this task has been deprecated and, while it is still functional, there will be no further support for this task and you should instead use the CASA task importasdm to import your dataset into CASA. In order to make importasdm duplicate the task importevla, several parameters will need to be set from their default values.

Where:
inp # lists the inputs available for this task
adsm='SDM-BDF File ID' # this is the filename of the SDM-BDF to use
vis='filename.ms' # this is the name of the output measurement set created (.ms)
ocorr_mode='co' # the VLA is a cross-correlator
savecmds=True # write the online flagging commands to an output file
outfile='filename.txt' # name of the file containing the online flags
applyflags=True # apply the online flags during creation of the MS
go # executes the task with the given inputs

Next we will use flagcmd to look at the table of online flags. This plot will show a graphical view of the online flags, which are antenna and/or time based flags.

Figure 1: Plot from flagcmd.

From this plot (see Figure 1), we can see that ea22 has a subreflector error during the beginning of the observation. We will flag this in a later step.

Step 3: Flag Antenna Shadowing, Zeros, and Very Bright Values

Next we will use flagdata to flag any antennas that may have been shadowed during the observation. This is a necessary step when observing in D- or C-configuration. Once we set mode='shadow' more parameters become available to edit specific to antenna shadowing, such as tolerance (the amount of shadow allowed (in meters)) and addantenna (file name or dictionary with additional antenna names, positions, and diameters). We will leave those parameters as the default settings of tolerance=0.0 (very conservative) and addantenna (no file or dictionary). Note, if an observation was taken in A- or B-configuration, this step is unnecessary.

The correlator is known to generate a small number of zeros in the data. We will use flagdata to remove those zeros and to clip the very bright values. Setting mode='clip' in flagdata will reveal new parameters specific to this mode. We will leave most of the parameters as the default settings, however we will set two in particular: correlation='ABS_ALL' will take the absolute value of RR and LL and clip the very bright values and clipzeros=True will clip the zero-value data generated by the correlator.

Step 4: Initial Inspection of the Dataset

The next step is to inspect the contents of the MS using listobs. The task listobs provides almost all relevant observational parameters such as correlator setup (frequencies, bandwidths, channel number and widths, polarization products), sources, scans, scan intents, and antenna locations. Setting verbose=True will display all of the contents of the raw data and setting listfile='listobs.txt' will create a text file you can refer to later.

Since the goal of this tutorial is to find the HI (1.420405752 GHz rest frequency) spectral line in LEDA 44055, note the spw ID containing the spectral line setup. From this particular instrument configuration, the spectral line setup is spw ID 0, while spw ID's 1-8 are continuum. Make note of the spectral line setup for spw 0. This information will be used later.